多个MapReduce之间的嵌套

在很多实际工作中，单个MR不能满足逻辑需求，而是需要多个MR之间的相互嵌套。很多场景下，一个MR的输入依赖于另一个MR的输出。结合案例实现一下两个MR的嵌套。
Tip：如果只关心多个MR嵌套的实现，可以直接跳到下面《多个MR嵌套源码》章节查看

案例描述

根据log日志计算log中不同的IP地址数量是多少。测试数据如下图所示：

该日志中每个字段都是用Tab建分割的。

案例分析

本次任务的目的是计算该日志不同的IP地址一共有多少。实现这个目的的方式有很多种，但是本文的目的是借助改案例对两个MapReduce之间的嵌套进行总结的。

实现方法

该任务分为两个MR过程，第一个MR（命名为MR1）负责将重复的ip地址去掉，然后将无重复的ip地址进行输出。第二个MR（命名为MR2）负责将MR1输出的ip地址文件进行汇总，然后将计算总数输出。

MR1阶段

map过程

public class IpFilterMapper extends Mapper<LongWritable, Text, Text, NullWritable> {    @Override    protected void map(LongWritable key, Text value,            Mapper<LongWritable, Text, Text, NullWritable>.Context context)            throws IOException, InterruptedException {        String line = value.toString();        String[] splits = line .split("\t");        String ip = splits[3];        context.write(new Text(ip), NullWritable.get());    }}

输入的key和value是文本的行号和每行的内容。
输出的key是ip地址，输出的value为空类型。

shuffle过程

主要是针对map阶段输出的key进行排序和分组，将相同的key分为一组，并且将相同key的value放到同一个集合里面，所以不同的组绝对不会出现相同的ip地址，分好组之后将值传递给reduce。注：该阶段是hadoop系统自动完成的，不需要程序员编程

reduce过程

 public class IpFilterReducer extends Reducer<Text, NullWritable, Text, NullWritable> {    @Override    protected void reduce(Text key, Iterable<NullWritable> values, Context context)             throws IOException, InterruptedException {        context.write(key, NullWritable.get());    }} 

由于经过shuffle阶段之后所有输入的key都是不同的，也就是ip地址是无重复的，所以可以直接输出。

MR2阶段

map过程

public class IpCountMapper extends Mapper<LongWritable, Text, Text, NullWritable> {    @Override    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, NullWritable>.Context context)            throws IOException, InterruptedException {        //输出的key为字符串"ip",这个可以随便设置，只要保证每次输出的key都一样就行        //目的是为了在shuffle阶段分组        context.write(new Text("ip"), NullWritable.get());    }}

shuffle过程

按照相同的key进行分组，由于map阶段所有的key都一样，所以最后只有一组。

reduce过程

public class IpCountReducer extends Reducer<Text, NullWritable, Text, NullWritable> {    @Override    protected void reduce(Text key, Iterable<NullWritable> values,            Reducer<Text, NullWritable, Text, NullWritable>.Context context) throws IOException, InterruptedException {        //用于存放ip地址总数量        int count = 0;        for (NullWritable v : values) {            count ++;        }        context.write(new Text(count+""), NullWritable.get());    }}

　流程图

源码

该案例所有源码都在下面。

MR1 map源码

//MR1 map源码package com.ipcount.mrmr;import java.io.IOException;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Mapper;public class IpFilterMapper extends Mapper<LongWritable, Text, Text, NullWritable> {    @Override    protected void map(LongWritable key, Text value,            Mapper<LongWritable, Text, Text, NullWritable>.Context context)            throws IOException, InterruptedException {        String line = value.toString();        String[] splits = line .split("\t");        String ip = splits[3];        context.write(new Text(ip), NullWritable.get());    }}

MR1 reduce源码

package com.ipcount.mrmr;import java.io.IOException;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Reducer;public class IpFilterReducer extends Reducer<Text, NullWritable, Text, NullWritable> {    @Override    protected void reduce(Text key, Iterable<NullWritable> values, Context context)             throws IOException, InterruptedException {        context.write(key, NullWritable.get());    }}

MR2 map源码

package com.ipcount.mrmr;import java.io.IOException;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Mapper;public class IpCountMapper extends Mapper<LongWritable, Text, Text, NullWritable> {    @Override    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, NullWritable>.Context context)            throws IOException, InterruptedException {        context.write(new Text("ip"), NullWritable.get());    }}

MR2 reduce源码

package com.ipcount.mrmr;import java.io.IOException;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Reducer;public class IpCountReducer extends Reducer<Text, NullWritable, Text, NullWritable> {    @Override    protected void reduce(Text key, Iterable<NullWritable> values,            Reducer<Text, NullWritable, Text, NullWritable>.Context context) throws IOException, InterruptedException {        int count = 0;        for (NullWritable v : values) {            count ++;        }        context.write(new Text(count+""), NullWritable.get());    }}

多个MR嵌套源码

package com.ipcount.mrmr;import java.io.IOException;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapred.JobConf;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.jobcontrol.ControlledJob;import org.apache.hadoop.mapreduce.lib.jobcontrol.JobControl;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;public class Driver {    public static void main(String[] args) throws Exception {        JobConf conf = new JobConf(Driver.class);        //job1设置        Job job1 = new Job(conf, "job1");        job1.setJarByClass(Driver.class);        job1.setMapperClass(IpFilterMapper.class);        job1.setMapOutputKeyClass(Text.class);        job1.setMapOutputValueClass(NullWritable.class);        job1.setReducerClass(IpFilterReducer.class);        job1.setOutputKeyClass(Text.class);        job1.setOutputValueClass(NullWritable.class);        FileInputFormat.setInputPaths(job1, new Path(args[0]));        FileOutputFormat.setOutputPath(job1, new Path(args[1]));        //job1加入控制器        ControlledJob ctrlJob1 = new ControlledJob(conf);        ctrlJob1.setJob(job1);        //job2设置        Job job2 = new Job(conf, "job2");        job2.setJarByClass(Driver.class);        job2.setMapperClass(IpCountMapper.class);        job2.setMapOutputKeyClass(Text.class);        job2.setMapOutputValueClass(NullWritable.class);        job2.setReducerClass(IpCountReducer.class);        job2.setOutputKeyClass(Text.class);        job2.setOutputValueClass(NullWritable.class);        FileInputFormat.setInputPaths(job2, new Path(args[1]));        FileOutputFormat.setOutputPath(job2, new Path(args[2]));        //job2加入控制器        ControlledJob ctrlJob2 = new ControlledJob(conf);        ctrlJob2.setJob(job2);        //设置作业之间的以来关系，job2的输入以来job1的输出        ctrlJob2.addDependingJob(ctrlJob1);        //设置主控制器，控制job1和job2两个作业        JobControl jobCtrl = new JobControl("myCtrl");        //添加到总的JobControl里，进行控制        jobCtrl.addJob(ctrlJob1);        jobCtrl.addJob(ctrlJob2);        //在线程中启动，记住一定要有这个        Thread thread = new Thread(jobCtrl);        thread.start();        while (true) {            if (jobCtrl.allFinished()) {                System.out.println(jobCtrl.getSuccessfulJobList());                jobCtrl.stop();                break;            }        }    }}